Update README.md

README.md

@@ -1,20 +1,114 @@
 ---
+base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
+library_name: transformers
 tags:
+- reasoning
+- chain-of-thought
+- deepseek
+- qwen
 - gguf
-- llama.cpp
+- bnb
+- 4bit
 - unsloth
+- chatml
+- agent
+- code
+- thinking
+- distilled
+license: apache-2.0
+---
+
+# 🌌 DeepLink-R1
+
+**DeepLink-R1** is a highly specialized, reasoning-focused large language model designed to act as a **"Logical Architect."** Built on top of the **`deepseek-ai/DeepSeek-R1-Distill-Qwen-7B`** architecture, this model doesn't just provide answers—it constructs transparent, mathematically rigorous blueprints of thought.
+
+It is trained to "think" before it speaks using `<think>` tags, exposing its internal logical deduction process before delivering a final, refined response. 
+
+Created by **3amthoughts**.
+
+### ⚡ Model Highlights
+* **Architecture:** 7B Parameters (Base: `deepseek-ai/DeepSeek-R1-Distill-Qwen-7B`)
+* **Format:** Available in BF16/FP16 (Transformers) and GGUF (Q4_K_M for local execution via llama.cpp/Ollama)
+* **Capabilities:** Deep logical reasoning, mathematical rigor, complex problem-solving, and transparent thought generation.
+* **Training:** Fine-tuned using QLoRA via Unsloth on a high-fidelity mixture of elite reasoning datasets.
+
+### 🎯 Key Capabilities
+* **Transparent Reasoning:** Trained to output its internal thought processes inside `<think>` tags, drastically reducing hallucinations and logical leaps.
+* **High-Tier Logic:** Excels at math, coding, and multi-step deduction by breaking down complex problems into manageable architectural blueprints.
+* **Lightweight & Fast:** Distilled into a 7B parameter footprint, making elite-level reasoning runnable on consumer hardware (MacBooks, RTX 3090/4090s).
 
 ---
 
-# DeepLink-R1-GGUF : GGUF
+### 🛠️ How it Works (Prompting & Output)
+To get the model to trigger its reasoning engine, you must use the standard ChatML template and prompt it to think step-by-step.
+
+**System Prompt:**
+> "You are a logical architect. Think step-by-step."
+
+**Example Interaction**
+**User:**
+> "If I have a 5-liter jug and a 3-liter jug, how can I measure exactly 4 liters of water?"
+
+**DeepLink-R1 Output:**
+```text
+<think>
+To measure exactly 4 liters using a 5-liter jug (J5) and a 3-liter jug (J3):
+1. Fill J5 to the top (J5 = 5L, J3 = 0L).
+2. Pour water from J5 into J3 until J3 is full. This takes 3 liters. (J5 = 2L, J3 = 3L).
+3. Empty J3 completely. (J5 = 2L, J3 = 0L).
+4. Pour the remaining 2 liters from J5 into J3. (J5 = 0L, J3 = 2L).
+5. Fill J5 to the top again. (J5 = 5L, J3 = 2L).
+6. Pour water from J5 into J3 until J3 is full. Since J3 already has 2 liters, it only takes 1 liter to fill it. (J5 = 4L, J3 = 3L).
+Now, J5 contains exactly 4 liters.
+</think>
+To measure exactly 4 liters, follow these steps:
+1. Fill the 5-liter jug completely.
+2. Pour water from the 5-liter jug into the 3-liter jug until it is full, leaving exactly 2 liters in the 5-liter jug.
+3. Empty the 3-liter jug.
+4. Pour the 2 liters from the 5-liter jug into the empty 3-liter jug.
+5. Refill the 5-liter jug completely.
+6. Carefully pour water from the 5-liter jug into the 3-liter jug until the 3-liter jug is full. Since it already contains 2 liters, it will only take 1 liter to fill.
+You now have exactly 4 liters of water remaining in the 5-liter jug.
+```
+
+
+💻 Prompt Format (ChatML)
+DeepLink-R1 strictly uses the ChatML prompt format.
+``` code
+Text
+<|im_start|>system
+You are a logical architect. Think step-by-step.<|im_end|>
+<|im_start|>user
+How many 'r's are in the word strawberry?<|im_end|>
+<|im_start|>assistant
+<think>
+...
+</think>
+...<|im_end|>
+```
+
+🚀 Usage
+Using transformers (Python)
+```code
+Python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "3amthoughts/DeepLink-R1"
 
-This model was finetuned and converted to GGUF format using [Unsloth](https://github.com/unslothai/unsloth).
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
 
-**Example usage**:
-- For text only LLMs:    `llama-cli -hf 3amthoughts/DeepLink-R1-GGUF --jinja`
-- For multimodal models: `llama-mtmd-cli -hf 3amthoughts/DeepLink-R1-GGUF --jinja`
+messages = [
+    {"role": "system", "content": "You are a logical architect. Think step-by-step."},
+    {"role": "user", "content": "How many 'r's are in the word strawberry?"}
+]
 
-## Available Model files:
-- `deepseek-r1-distill-qwen-7b.Q4_K_M.gguf`
-This was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth)
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to("cuda")
+outputs = model.generate(inputs, max_new_tokens=1024, temperature=0.6)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```